DrapeNet: Garment Generation and Self-Supervised Draping

This is the repo for DrapeNet: Garment Generation and Self-Supervised Draping.

Environment:

• Ubuntu 20.04
• python 3.8.6
• PyTorch 1.13.1 w/ CUDA 11.7

Setup:

Option I: Using virtualenv

python3 -m venv .venv
source .venv/bin/activate
pip install -U pip setuptools
pip install numpy open3d einops hesiod fvcore tensorboard trimesh cython networkx

Install torch and pytorch3d:

pip install https://download.pytorch.org/whl/cu113/torch-1.12.0%2Bcu113-cp38-cp38-linux_x86_64.whl
pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py38_cu113_pyt1120/download.html

Build and install meshudf:

cd meshudf
source setup.sh

Download and extract the SMPL model from http://smplify.is.tue.mpg.de/ and place basicModel_f_lbs_10_207_0_v1.0.0.pkl in the folder of ./smpl_pytorch manually or:

bash download_smpl.sh

Option II: Using docker

cd docker/ && docker build -t drapenet . && cd ..

Run the container by:

docker run -it --rm --gpus all --shm-size=8gb --name drapenet drapenet

Once inside the docker container, download and extract the SMPL model from http://smplify.is.tue.mpg.de/ and place basicModel_f_lbs_10_207_0_v1.0.0.pkl in the folder of ./smpl_pytorch manually or:

bash download_smpl.sh

We would like to thank Kristijan Bartol for the contribution of this installation setup.

Encoder-Decoder:

cd encdec
python3 preprocess_udf.py </path/to/meshes> </out/path>
python3 train_encdec.py  # add </path/to/run/cfg/file> to restore training
python3 export_codes.py </path/to/run/cfg/file>
python3 export_meshes.py </path/to/run/cfg/file>

The mesh data for the training of Encoder-Decoder can be downloaded from here. Note that we preprocess the orignal meshes of CLOTH3D to drape them on the female body with neutral shape.

Editing:

cd editing
python3 create_dset.py
python3 compute_weights.py
python3 edit.py

Draping:

cd draping
python3 drape.py

Citation

If you find our work useful, please cite it as:

@inproceedings{de2023drapenet,
  author = {De Luigi, Luca and Li, Ren and Guillard, Benoit and Salzmann, Mathieu and Fua, Pascal},
  title = {{DrapeNet: Garment Generation and Self-Supervised Draping}},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year = {2023}
}

Our work is built upon MeshUDF. If you find it helpful, please cite it properly as well.